To Centralize Analytics or Not, That is the Question

This is a contributed article by my colleague, Mukul who is Aryng's main instructor on Experimentation.

The structure of analytics in large organizations can take many forms—from having a gazillion analytics micro-teams embedded in each function or BU, to completely centralized analytics at the corporate level. What is the right strategy? What should your organization do?

Well, in that respect, the title of this post is misleading. To centralize or not to centralize, is actually NOT the question. If you think of centralization on a scale going from ‘not at all’ to ‘fully centralized’, the real question is what is the right level for you?

To answer that question you must be aware of the pros and cons of moving one way or the other on that scale. Having been a part of multiple “re-orgs” and that have gone up and down on the scale, and having influenced some of those movements some of the time, I have some first hand insight into this.

So here are the top 5 key trade-offs when faced with organizational structure of analytics.

1. Consultant Mindset vs. Deep Personal Investment: God bless consultants, they often save the day! But one thing they cannot claim is deep emotional investment in the organization they are working for. This is what high degree of centralization does. Analysts are assigned to BU’s or functions based on prioritization of the project and resource constraints. Their mindset is like that of a consultant, where you work on a project, crunch the numbers, deliver the insights and you job is done… time to move on to the next one. With analytics embedded within the function, there can be full integration of analytics with the project right from its conception. The alignment of purpose this creates, produces very non-linear synergistic effects with respect to the value derived from analytics. This alignment/ownership, of course could be a problem by itself, which brings us to the next point

2. Objectivity (or at least the perception of it): If the analytics team reports into the owner of the domain, and their rewards are aligned with the success of the projects being analyzed, the objectivity of the analysis could be in question. The analyst could potentially introduce a bias to make the project/initiative look better than it actually is. With analytics, credibility is everything. The perception of lack of objectivity could be devastating for the entire group/organization. If you believe that numbers cannot lie, you are either not in the field of analytics or are deluded. Read How To Lie With Statistics for starters.